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About

Yufei Xue is an emerging robotics researcher whose work sits at the intersection of reinforcement learning and legged robot locomotion, with a particular focus on enabling quadrupedal and legged robots to move naturally, robustly, and agilely across complex terrains. His most impactful contribution, "Learning Robust and Agile Legged Locomotion Using Adversarial Motion Priors" (2023, 90 citations), introduced the first blind locomotion system capable of traversing challenging terrains without relying on external sensing, leveraging Adversarial Motion Priors to produce both high-speed and terrain-adaptive behavior. Building on this foundation, Xue has explored multi-gait learning through latent space representations, developing frameworks that allow legged robots to seamlessly transition between gaits in response to varying terrains and velocity commands — work documented across multiple publications from 2023 to 2024. His experience-inspired two-step reward methodology further demonstrates a creative approach to curriculum design, drawing analogies from biological learning to progressively train robots toward naturalistic movement. With a growing citation record and a consistent focus on bridging biological motion principles with data-driven control, Xue represents a promising voice in the next generation of legged robotics researchers.